its a cyber security application to save organisation from mail fishing spoofing also protect from brand forgeries its a beautiful analytical dashboard to track ip level with detail report of day to day activities as well as give a over all score of your brand

mykidsclub is video streaming platform just like YouTube with extra cdild and parents control with special privileges child login

food delivery application with admin dashboard and multi tenant structure

Developed a comprehensive payment gateway integration platform utilizing Django and Python, focusing on scalability and security to handle high-volume transactions. This project involved deep integration with AWS services, including EC2 for compute, Lambda for serverless functions, and S3 for storage, ensuring a robust infrastructure capable of supporting significant transaction loads. By implementing a microservices architecture, containerizing services with Docker, and adopting continuous integration/continuous deployment (CI/CD) practices, we achieved a 30% increase in transaction volume. This platform significantly enhanced the operational efficiency and security of payment processing, contributing to improved customer satisfaction and trust.

Led the development of an automated financial analysis tool designed to process large datasets and generate comprehensive reports. The tool leveraged Python's pandas and NumPy libraries for data manipulation and analysis, deployed on AWS EC2 for scalable compute resources. The challenge was to automate the extraction, transformation, and loading (ETL) processes, ensuring accuracy and efficiency in financial reporting. By integrating AWS Lambda for event-driven data processing and utilizing S3 for data storage, we streamlined operations, reducing report generation time by 40%. This project not only improved the speed and reliability of financial analysis but also enabled more data-driven decision-making for our clients.

Tasked with optimizing an existing e-commerce platform, I led a project that focused on enhancing user experience and performance. Using Django for the backend, we integrated HTML, CSS, and JavaScript to improve the frontend design and interactivity. One significant challenge was ensuring the platform could handle increased traffic without compromising speed or reliability. Through Docker for containerization and AWS services like EC2 and S3 for scalable web hosting, we managed to achieve a more resilient and efficient platform. The optimization efforts resulted in faster page loads, improved SEO rankings, and a noticeable increase in user engagement and sales.

Engineered a real-time analytics dashboard utilizing the Django REST Framework to provide immediate insights into business metrics. The deployment on AWS EC2, coupled with Docker containerization, facilitated a scalable and reliable solution. The project presented challenges in processing and visualizing data in real-time, requiring efficient backend logic and a responsive frontend. By employing WebSocket for live data updates and integrating various data visualization libraries, we enabled stakeholders to make informed decisions swiftly. This dashboard became a crucial tool for monitoring performance, trends, and operational health, enhancing strategic planning and operational agility.

Contributed to the development of a comprehensive healthcare management system, focusing on enhancing patient care and operational efficiency. The system was built using Django, with a frontend leveraging HTML, CSS, and JavaScript for an intuitive user experience. Integrating AWS services like EC2, Lambda, and S3, we addressed challenges in data security, scalability, and accessibility. The implementation of machine learning algorithms for predictive analytics and the use of Docker for deployment streamlined healthcare processes, improved patient outcomes, and reduced operational costs. This project demonstrated the potential of technology to transform healthcare services, making them more accessible, efficient, and patient-centric.

Led the design and implementation of a customer segmentation model to personalize marketing strategies. Applied unsupervised learning techniques to categorize customers, which increased campaign conversion rates by 30%.

Led the design and implementation of a customer segmentation model to personalize marketing strategies. Applied unsupervised learning techniques to categorize customers, which increased campaign conversion rates by 30.

Created a sentiment analysis tool to gauge customer sentiment from social media data. This tool informed product development and marketing strategies, resulting in a 15% increase in customer satisfaction scores.

Developed a churn prediction model to forecast customer attrition. The model's insights led to targeted retention strategies, reducing churn by 20%.